Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean Deliveries

重新思考电子胎儿监护以改善围产期结局并减少阴道手术和剖腹产的频率

• 批准号: 10380847
• 负责人: Petar M Djuric
• 金额: $ 66.33万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-10 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10380847
• 关键词: Address; Adoption; Algorithms; Apgar Score; Architecture; Asphyxia; Bayesian Method; Biological Markers; Blood flow; Brain Hypoxia-Ischemia; Brain Injuries; Categories; Cerebral Palsy; Cesarean section; Classification; Clinical; Clinical Data; Complex; Computer software; Computers; Data; Data Analyses; Databases; Decision Making; Decision Support Systems; Development; Disabled Persons; Drops; Engineering; Evaluation; Fetal Death; Fetal Heart Rate; Fetal Monitoring; Fetus; Frequencies; Future; Gaussian model; Goals; Health; Heart Rate; Hypoxia; Infant; Intervention; Knowledge; Lead; Machine Learning; Medical Staff; Metabolic acidosis; Methodology; Methods; Monitor; Neonatal Mortality; Nervous System Trauma; Network-based; Neurological outcome; Newborn Infant; Outcome; Oxygen; Patients; Pattern; Performance; Personal Satisfaction; Physiological; Predictive Analytics; Predictive Value; Process; Records; Research; Role; Science; Scientist; Signal Transduction; Source; System; Technology; Testing; Time; Training; Umbilical cord structure; University Hospitals; Uterus; Vagina; Vaginal delivery procedure; Vision; Visual; Work; adverse outcome; antenatal; base; computerized; data anonymization; deep learning; fetal; fetal medicine; fetus at risk; fetus hypoxia; heart rate monitor; improved; intrapartum; ischemic injury; learning strategy; machine learning method; neonatal seizure; nervous system disorder; online repository; outcome prediction; perinatal outcomes; prevent; public repository; repository; signal processing; stem; stillbirth; theories

The essential role of electronic fetal monitoring (EFM) during labor is to prevent adverse outcomes due to fetal hypoxia and ischemia. Its established weaknesses include: 1) the obstetrician’s highly subjective visual interpretations of the signal patterns and 2) the widespread use of unproven surrogates for relevant fetal hypoxic and/or ischemic injury such as umbilical arterial pH, intrapartum stillbirth, newborn Apgar scores and neonatal seizures. This technology over the past 50 years has not been shown to decrease stillbirths or reduce the numbers of infants with cerebral palsy. EFM as it is presently used in the clinical setting has been associated with an extraordinary increase in the use of operative vaginal delivery and cesarean delivery. No functional algorithm has yet been developed that integrates clinical data collected in the antepartum period and during labor and any other patient specific data with the results of EFM. The main objective of the proposed research is to use recent breakthroughs in machine learning to drive the development of predictive analytics to support and improve the interpretation of EFM data, especially under real world conditions and in real time where clinicians must make timely decisions about interventions to prevent adverse outcomes. It is anticipated that the proposed research will result in significantly decreased use of operative vaginal delivery and cesarean delivery while more precisely defining the fetus at risk for developing metabolic acidosis and long term neurologic injury.

劳动期间电子胎儿监测（EFM）的基本作用是防止不利的结果 胎儿缺氧和缺血。它既定的弱点包括：1）产科医生的高度主观 信号模式的视觉解释和2）未经证实的替代物的宽度使用 相关的胎儿低氧和/或缺血性损伤，例如脐动脉pH，肠内死产， 新生儿Apgar得分和新生儿癫痫发作。在过去的50年中，这项技术还没有 显示可减少死产或减少脑瘫的婴儿人数。 EFM，因为目前是 在临床环境中使用与操作的使用相关 阴道分娩和剖宫产。尚未开发任何功能算法来集成 在产前期和分娩期间和任何其他患者特定数据期间收集的临床数据 与EFM的结果。拟议研究的主要目的是在 机器学习以推动预测分析的开发以支持和改善 EFM数据的解释，尤其是在现实世界条件下以及临床医生必须实时的解释 及时就干预措施做出决定，以防止不良后果。预计 拟议的研究将显着改善使用阴道分娩和剖宫产的使用 交付的同时更精确地定义了胎儿患有代谢性酸中毒的风险和长期 神经损伤。